Inferring Causal Relations Observational Data
December 14, 2022
Mental disorder is produced by direct causal interactions between symptoms that reinforce each other via feedback loops. (Borsboom & Cramer, 2013).
Directed Acyclic Graph (DAG)
Directed Cyclic Graph (DCG)
Problem: Estimating a cyclic causal model is fundamentally very difficult. Relaxing the acyclicity assumption entails much of theoretical complication.
Cyclic Causal Discovery (CCD) (Richardson, 1996)
Fast Causal Inference (FCI) (M. Mooij & Claassen, 2020)
Cyclic Causal Inference (CCI) (Strobl, 2019)
PAG: Partial Ancestral Graph ?
PAAG: Partially-oriented MAAG ?
MAAG: Maximal Almost Ancestral Graph ?
Causal inference is the fundamental interest in science.
The underlying dynamic processes of many systems contain cycles.
Learning cyclic causal models from observational data is challenging.
Our study will showcase the cyclic causal discovery algorithms that are potentially suitable for typical psychological observational data.
Global Markov property is no longer guaranteed.
Cyclic model is not always statistically identified (even in linear case).
Equilibrium state is necessary (All \(|\lambda| < 1\)).
Personalized psychotherapy (target symptoms)
Medical: effective treatment design
Possible combination with different types of causal discovery algorithm. \(\rightarrow\) Hybrid!
CCD+ GES (greedy equivalence search)
Methodology and Statistics for the Behavioural, Biomedical and Social Sciences